Optimal sequential Bayesian analysis for degradation tests

Silvia Rodríguez-Narciso () and J. Andrés Christen ()
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Silvia Rodríguez-Narciso: Universidad Autónoma de Aguascalientes
J. Andrés Christen: Centro de Investigación en Matemáticas

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 3, No 5, 405-428

Abstract: Abstract Degradation tests are especially difficult to conduct for items with high reliability. Test costs, caused mainly by prolonged item duration and item destruction costs, establish the necessity of sequential degradation test designs. We propose a methodology that sequentially selects the optimal observation times to measure the degradation, using a convenient rule that maximizes the inference precision and minimizes test costs. In particular our objective is to estimate a quantile of the time to failure distribution, where the degradation process is modelled as a linear model using Bayesian inference. The proposed sequential analysis is based on an index that measures the expected discrepancy between the estimated quantile and its corresponding prediction, using Monte Carlo methods. The procedure was successfully implemented for simulated and real data.

Keywords: Degradation tests; Bayesian analysis; Sequential analysis; Monte Carlo methods (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-015-9339-7

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